Resilient Backpropagation Neural Network on Prediction of Poverty Levels in South Sulawesi

Keywords: Accuracy, Resilient Algorithm, Backpropagation Neural Network, Proverty Levels, Prediction


Poverty is a topic that continues and is always discussed up to this time, as a benchmark indicator of how the level of welfare and prosperity in the lives of people in a country. Several attempts have been made by the central and regional governments to reduce poverty levels, including “Bantuan Langsung Tunai” (BLT) and the “Program Keluarga Harapan” (PKH). However, poverty reduction in Indonesia is still slowing down, including in South Sulawesi. Based on this, this study aims to predict poverty levels in South Sulawesi. Factors thought to influence poverty levels are the Human Development Index (HDI), the Open Unemployment Rate (TPT), and the Gross Regional Domestic Product (GRDP). The data used are data from 2010 to 2014. The method used is a backpropagation neural network with a resilient algorithm or better known as a resilient backpropagation neural network (RBNN). The results of the prediction of poverty levels using predictors of HDI, TPT, and GRDP showed that the analysis of the RBNN reached its optimum using architecture [3- 9 - 1] and reached convergence at the 81th iteration with an accuracy rate of 95.34%.


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How to Cite
Poerwanto, B., & Fajriani, F. (2020). Resilient Backpropagation Neural Network on Prediction of Poverty Levels in South Sulawesi. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 20(1), 11-18.